Optimal network structures
Complexity Speaker Series
Clara Stegehuis
Associate Professor, Twente University
Past Talk
Hybrid talk
Friday
Apr 26, 2024
Watch video
11:00 am
EST
Virtual
177 Huntington Ave.
11th floor
11th floor
Devon House
58 St Katharine's Way
London E1W 1LP, UK
58 St Katharine's Way
London E1W 1LP, UK
Subgraphs contain important information about network structures and their functions. But where can we find these subgraphs in random graphs? We investigate this by using optimization problems that identifies the dominant structure of any given subgraph. The optimizer describes the degrees and the spatial locations of the vertices that together create the most likely subgraph. On the popular hyperbolic random graph model, our optimization method shows the trade-off between geometry and popularity: some subgraphs are most likely formed by vertices that are close by, whereas others are most likely formed by vertices of high degree. This insight makes it possible to create new statistics that detect the presence of an underlying hyperbolic spatial structure, and is also able to detect global network phenomena such as maximal clique structures.
About the speaker
About the speaker
I am an associate professor at Twente University. I work at the intersection of probability theory, graph theory and stochastic networks, with an emphasis on asymptotic analysis, stochastic process limits, and randomized algorithms. Problems I investigate are inspired by applications in network science, physics and computer science. Some of my research interests are 1) epidemic spreading and percolation, 2) network motifs, 3) asymptotic network properties, and 4) geometric networks.
I am an associate professor at Twente University. I work at the intersection of probability theory, graph theory and stochastic networks, with an emphasis on asymptotic analysis, stochastic process limits, and randomized algorithms. Problems I investigate are inspired by applications in network science, physics and computer science. Some of my research interests are 1) epidemic spreading and percolation, 2) network motifs, 3) asymptotic network properties, and 4) geometric networks.
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